Invisible clinical labor driving the successful integration of AI in healthcare
Artificial Intelligence and Machine Learning (AI/ML) tools are changing the landscape of healthcare decision-making. Vast amounts of data can lead to efficient triage and diagnosis of patients with the assistance of ML methodologies. However, more research has focused on the technological challenges...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Computer Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcomp.2022.1045704/full |
| _version_ | 1828123955090685952 |
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| author | Mara Ulloa Blaine Rothrock Faraz S. Ahmad Faraz S. Ahmad Maia Jacobs Maia Jacobs |
| author_facet | Mara Ulloa Blaine Rothrock Faraz S. Ahmad Faraz S. Ahmad Maia Jacobs Maia Jacobs |
| author_sort | Mara Ulloa |
| collection | DOAJ |
| description | Artificial Intelligence and Machine Learning (AI/ML) tools are changing the landscape of healthcare decision-making. Vast amounts of data can lead to efficient triage and diagnosis of patients with the assistance of ML methodologies. However, more research has focused on the technological challenges of developing AI, rather than the system integration. As a result, clinical teams' role in developing and deploying these tools has been overlooked. We look to three case studies from our research to describe the often invisible work that clinical teams do in driving the successful integration of clinical AI tools. Namely, clinical teams support data labeling, identifying algorithmic errors and accounting for workflow exceptions, translating algorithmic output to clinical next steps in care, and developing team awareness of how the tool is used once deployed. We call for detailed and extensive documentation strategies (of clinical labor, workflows, and team structures) to ensure this labor is valued and to promote sharing of sociotechnical implementation strategies. |
| first_indexed | 2024-04-11T14:59:48Z |
| format | Article |
| id | doaj.art-a636bec09adb4e14ad416fb324c7b3ac |
| institution | Directory Open Access Journal |
| issn | 2624-9898 |
| language | English |
| last_indexed | 2024-04-11T14:59:48Z |
| publishDate | 2022-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computer Science |
| spelling | doaj.art-a636bec09adb4e14ad416fb324c7b3ac2022-12-22T04:17:03ZengFrontiers Media S.A.Frontiers in Computer Science2624-98982022-12-01410.3389/fcomp.2022.10457041045704Invisible clinical labor driving the successful integration of AI in healthcareMara Ulloa0Blaine Rothrock1Faraz S. Ahmad2Faraz S. Ahmad3Maia Jacobs4Maia Jacobs5Department of Computer Science, Northwestern University, Evanston, IL, United StatesDepartment of Computer Science, Northwestern University, Evanston, IL, United StatesDepartment of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United StatesDepartment of Preventive Medicine, Northwestern University, Evanston, IL, United StatesDepartment of Computer Science, Northwestern University, Evanston, IL, United StatesDepartment of Preventive Medicine, Northwestern University, Evanston, IL, United StatesArtificial Intelligence and Machine Learning (AI/ML) tools are changing the landscape of healthcare decision-making. Vast amounts of data can lead to efficient triage and diagnosis of patients with the assistance of ML methodologies. However, more research has focused on the technological challenges of developing AI, rather than the system integration. As a result, clinical teams' role in developing and deploying these tools has been overlooked. We look to three case studies from our research to describe the often invisible work that clinical teams do in driving the successful integration of clinical AI tools. Namely, clinical teams support data labeling, identifying algorithmic errors and accounting for workflow exceptions, translating algorithmic output to clinical next steps in care, and developing team awareness of how the tool is used once deployed. We call for detailed and extensive documentation strategies (of clinical labor, workflows, and team structures) to ensure this labor is valued and to promote sharing of sociotechnical implementation strategies.https://www.frontiersin.org/articles/10.3389/fcomp.2022.1045704/fullartificial intelligencehealthcaresociotechnical systemsdecision support systemshuman-AI collaboration |
| spellingShingle | Mara Ulloa Blaine Rothrock Faraz S. Ahmad Faraz S. Ahmad Maia Jacobs Maia Jacobs Invisible clinical labor driving the successful integration of AI in healthcare Frontiers in Computer Science artificial intelligence healthcare sociotechnical systems decision support systems human-AI collaboration |
| title | Invisible clinical labor driving the successful integration of AI in healthcare |
| title_full | Invisible clinical labor driving the successful integration of AI in healthcare |
| title_fullStr | Invisible clinical labor driving the successful integration of AI in healthcare |
| title_full_unstemmed | Invisible clinical labor driving the successful integration of AI in healthcare |
| title_short | Invisible clinical labor driving the successful integration of AI in healthcare |
| title_sort | invisible clinical labor driving the successful integration of ai in healthcare |
| topic | artificial intelligence healthcare sociotechnical systems decision support systems human-AI collaboration |
| url | https://www.frontiersin.org/articles/10.3389/fcomp.2022.1045704/full |
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